GraphRAG explains why AI is shifting from isolated text to connected knowledge, and what that means for AI search optimization. Making your brand machine-readable and increasing its chances of being ...

A context graph could capture the full context, reasoning, and causal relationships behind critical business decisions. It’s a powerful idea. A December 2025 paper from Silicon Valley venture capital ...

😭 GraphRAG is good and powerful, but the official implementation is difficult/painful to read or hack. 😊 This project provides a smaller, faster, cleaner GraphRAG, while remaining the core ...

Researchers affiliated with universities in China and Singapore have devised a technique to make stolen knowledge graph data useless if incorporated into a GraphRAG AI system without consent. Large ...

For decades the data landscape was relatively static. Relational databases (hello, Oracle!) were the default and dominated, organizing information into familiar columns and rows. That stability eroded ...

Abstract: This paper introduces MedImgGraphRag-Corrector, a hierarchical hybrid framework that combines a specialized medical imaging dictionary with GraphRAG-enhanced knowledge inference to correct ...

What if your AI could not only retrieve information but also uncover the hidden relationships that make your data truly meaningful? Traditional vector-based retrieval methods, while effective for ...

This project is maintained again as of 2026-06. The current goal is to keep the original py2neo v3 / Neo4j 3.x example usable for learners, notebooks, and legacy projects while adding a current Neo4j ...

To gain competitive advantage from gen AI, enterprises need to be able to add their own expertise to off-the-shelf systems. Yet standard enterprise data stores aren't a good fit to train large ...

While retrieval-augmented generation is effective for simpler queries, advanced reasoning questions require deeper connections between information that exist across documents. They require a knowledge ...